Our long-term goal is to understand how experience shapes neural mechanisms of visual perception. We previously showed that months of training can improve sensitivity on a visual discrimination task via an increasingly selective readout of the most sensitive neurons in a sensory population. Performance on this and other visual tasks can also depend strongly on the temporal dynamics of visual processing. We propose to study the computational principles and neural mechanisms that govern how these temporal dynamics are shaped by recent experience to optimize perceptual sensitivity in a dynamic environment. We build on a theoretical framework we developed recently that describes optimal information accumulation in dynamic environments. Using the framework as a starting point, we will test two primary hypotheses. First, the temporal dynamics of visual processing reflect learned expectations about the temporal dynamics of the relevant inputs, consistent with the optimal model. Second, this process involves two complementary mechanisms that have long been known to contribute to change detection but whose specific, computational roles in shaping the learned temporal dynamics of visual processing are not yet known: sensory adaptation in visual cortex and the processing of uncertainty in the anterior cingulate cortex (ACC) and related arousal systems. We test these hypotheses using three Specific Aims. First, we will establish roles for sensory adaptation and arousal in optimizing the temporal dynamics of visual motion processing using human psychophysics and computational modeling. Second, we will determine how history-dependent modulation of adaptation dynamics of motion- sensitive neurons in the middle temporal area (MT) of extrastriate visual cortex can contribute to the temporal dynamics of visual motion processing in monkeys. Three, we will determine how the representation of expected and unexpected changes in stimulus dynamics represented in the ACC can contribute to the temporal dynamics of visual processing in monkeys, particularly in terms of recognizing change-points that reset evidence accumulation. Each Aim alone will provide new insights into how sensory adaptation and ACC- mediated computations can affect the temporal dynamics of visual processing. Together, these studies will provide a novel, unified view of how these mechanisms can interact to help optimize how the brain processes dynamic visual input over time.